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Estimation in semiparametric models with missing data
Copulas imputation kernel smoothing missing at random nuisance function partially linear model
2016/1/25
We propose a novel varying coefficient model, called princi-pal varying coefficient model (PVCM), by characterizing the varying coeffi-cients through linear combinations of a few principal functions. ...
Nonparametric and Semiparametric Regressions Subject to Monotonicity Constraints: Estimation and Forecasting
Nonlinearity Nonparametric regression Semiparametric regression Local mono- tonicity Bagging
2016/1/20
This paper considers nonparametric and semiparametric regression models subject to monotonicity constraint. We use bagging as an alternative approach to Hall and Huang(2001). Asymptotic properties of ...
Dynamic Large Spatial Covariance Matrix Estimation in Application to Semiparametric Model Construction via Variable Clustering: the SCE approach
Time Series Covariance Estimation Regularization, Sparsity
2011/7/6
To better understand the spatial structure of large panels of economic and financial time series and provide a guideline for constructing semiparametric models, this paper first considers estimating a...